IMAGE DETECTION METHOD AND IMAGE DETECTION DEVICE utilizing dual analysis

ABSTRACT

An image detection method is provided. In the image detection method, images of a user are obtained, feature parameters are marked in the images, and detection results of the feature parameters in each of the images are evaluated. A body distribution analysis is performed on the images according to the detection result of at least one first feature parameter among the feature parameters to determine first position information of the user. A face occlusion analysis is performed on the images according to the detection result of at least one second feature parameter among the feature parameters and the first position information to determine second position information of the user. The at least one second feature parameter is different from the at least one first feature parameter. The second position information represents a position of the user.

CROSS REFERENCE TO RELATED APPLICATIONS

This Application claims priority of Taiwan Patent Application No.107130638, filed on Aug. 31, 2018, the entirety of which is/areincorporated by reference herein.

BACKGROUND OF THE INVENTION Field of the Invention

The invention relates to an image detection method and an imagedetection device, and more particularly to an image detection method andan image detection device for determining positions of users.

Description of the Related Art

Many detection techniques can be applied to determine physiologicalparameters and body position of a user for the purpose of monitoring andcaring for a baby, a child, a patient, or an elderly person. Althoughthe sleeping position of a user can be determined by detecting facialfeatures, facial features are rather unstable. There are more featuresin the frontal face. Thus, the detection rate is higher, and thedetection rate of the lateral face is much lower.

Although the physiological information of the user can be obtained bywearing a smart wearable device, such as a wristband, there may be theproblem of insufficient power. In addition, wearing a smart wearabledevice may be inconvenient or make the user feel uncomfortable.Therefore, there is a need for an image detection method and an imagedetection device capable of improving recognition and detectionefficiency.

BRIEF SUMMARY OF THE INVENTION

In order to solve the above problems, the present invention provides animage detection method and an image detection device for determining aposition of a user. In the present invention, a plurality of featureparameters of the images of the user are obtained, and a bodydistribution analysis and a face occlusion analysis are performed todetermine the position of the user. The image detection method providedby the present invention adopts an artificial intelligence neuralnetwork architecture, and performs a dual analysis of the bodydistribution and the face occlusion, so as to accurately determine theposition of the user, thereby achieving the purpose of care.

One embodiment of the present invention provides an image detectionmethod for determining the position of a user. The image detectionmethod comprises the steps of obtaining a plurality of images of theuser; determining whether the user moves according to the images;obtaining a plurality of feature parameters of the plurality of image;and performing a body distribution analysis and a face occlusionanalysis on the plurality of images according to the feature parametersto determine the position of the user.

In detail, the image detection method of the present invention furthercomprises the steps of dividing each of the plurality of images of theuser into a plurality of region bases; calculating detection results ofthe plurality of feature parameters in each of the plurality of regionbases; and determining the position of the user according to thedetection results. The image detection method of the present inventionalso comprises the following step: in response to determining that theposition of the user is sleeping on his side or in the prone positionand a determined confidence level being lower than a predeterminedconfidence level, determining or modifying the position of the useraccording to the result of a face occlusion analysis.

Another embodiment of the present invention provides an image detectionmethod for determining the position of a user. The image detectionmethod comprises the steps of obtaining a plurality of images of theuser; marking a plurality of feature parameters in the plurality ofimages; evaluating detection results of the plurality of featureparameters in each of the plurality of images; performing a bodydistribution analysis on the plurality of images according to thedetection result of at least one first feature parameter among theplurality of feature parameters to determine first position informationof the user; and performing a face occlusion analysis on the pluralityof images according to the detection result of at least one secondfeature parameter among the plurality of feature parameters and thefirst position information to determine second position information ofthe user. The at least one second feature parameter is different fromthe at least one first feature parameter, and the second positioninformation represents the posture of the user.

Another embodiment of the present invention provides an image detectionmethod for determining the position of a user. The image detectionmethod comprises the steps of obtaining a plurality of images of theuser; obtaining a plurality of feature parameters of the plurality ofimages; performing a face occlusion analysis on the plurality of imagesaccording to the plurality of feature parameters to determine whetherthe plurality of images clearly show the user's face; determining aplurality of feature vectors and performing a body distribution analysison the plurality of images according to the plurality of feature vectorsto determine a body position and a position type of the user; andselecting an image regarding the position type according to results ofthe face occlusion analysis and the body distribution analysis.

Another embodiment of the present invention provides an image detectiondevice for determining the position of a user. The image detectiondevice comprises a sensor, a notification device, and a processor. Thesensor captures a plurality of images of the user. The processordetermines whether the user moves according to the plurality of imagesand obtains a plurality of feature parameters of the plurality ofimages. The processor performs a body distribution analysis and a faceocclusion analysis on the images according to the feature parameters todetermine the position of the user.

Another embodiment of the present invention provides an image detectiondevice for determining the position of a user. The image detectiondevice comprises a sensor and a processor. The processor comprises abody distribution analysis module and a face occlusion analysis. Thesensor capturing a plurality of images of the user. The data markingmodule marks a plurality of feature parameters in the plurality ofimages. The feature analysis module calculates detection results of theplurality of feature parameters in each of the plurality of images. Thebody distribution analysis module performs a body distribution analysison the plurality of images according to the detection result of at leastone first feature parameter among the plurality of feature parameters todetermine first position information of the user. The face occlusionanalysis module performs a face occlusion analysis on the plurality ofimages according to the detection result of at least one second featureparameter among the plurality of feature parameters and the firstposition information to determine second position information of theuser. The at least one second feature parameter is different from the atleast one first feature parameter, and the second position informationrepresents the position of the user.

Another embodiment of the present invention provides an image detectiondevice for determining the position of a user. The image detectiondevice comprises a sensor and a processor. The sensor captures aplurality of images of the user. The processor comprises a data markingmodule, a body distribution analysis module, and a face occlusionanalysis module. The data marking module obtains a plurality of featureparameters of the plurality of images. The face occlusion analysismodule performs a face occlusion analysis on the plurality of imagesaccording to the plurality of feature parameters to determine whetherthe plurality of images clearly show the user's face. The bodydistribution analysis module determines a plurality of feature vectorsand performs a body distribution analysis on the plurality of imagesaccording to the plurality of feature vectors to determine a bodyposition and a position type of the user. The processor selects an imagerelated to the position type according to results of the face occlusionanalysis and the body distribution analysis.

With regard to other additional features and advantages of the presentinvention, those skilled in the art can use the image detection methodand the image detection device disclosed in the method of the presentinvention without departing from the spirit and scope of the presentinvention.

A detailed description is given in the following embodiments withreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The full disclosure is based on the following detailed description andin conjunction with the drawings. It should be noted that theillustrations are not necessarily drawn to scale in accordance with thegeneral operation of the industry. In fact, it is possible toarbitrarily enlarge or reduce the sizes of the components for a clearexplanation.

FIG. 1A is a schematic diagram showing an image detection deviceaccording to an exemplary embodiment of the present invention;

FIG. 1B is a schematic diagram showing an image detection deviceaccording to another exemplary embodiment of the present invention;

FIG. 1C is a schematic diagram showing a processor and a notificationdevice according to another exemplary embodiment of the presentinvention;

FIG. 2 is a flow chart showing an image detection method according to anexemplary embodiment of the present invention;

FIG. 3 is a schematic diagram showing a plurality of region basesaccording to an exemplary embodiment of the present invention;

FIGS. 4A and 4B are schematic diagrams showing marking of featureparameters according to an exemplary embodiment of the presentinvention;

FIG. 5 is a schematic diagram showing feature parameters being obtainedfrom an image of the user according to an exemplary embodiment of thepresent invention;

FIGS. 6A and 6B are schematic diagrams showing feature vectors accordingto an exemplary embodiment of the present invention;

FIG. 7 shows a flow chart of determination of a risk from imagesaccording to an risk embodiment of the invention;

FIG. 8 shows a flow chart of a body distribution analysis and a faceocclusion analysis according to an exemplary embodiment of the presentinvention;

FIGS. 9A-9E show a flow chart of determination of a position of the useraccording to a collaborative body distribution analysis and a faceocclusion analysis according to an exemplary embodiment of the presentinvention; and

FIG. 10 shows a flow chart of determination of a representative imagefor a position type by using an image detection method according to anexemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The following description is of the best-contemplated mode of carryingout the invention. This description is made for the purpose ofillustrating the general principles of the invention and should not betaken in a limiting sense. The scope of the invention is best determinedby reference to the appended claims.

The following description provides many different embodiments orexamples to implement various features of the present invention. Thefollowing description sets forth specific examples of various componentsand their arrangement to simplify the description. Of course, thesespecific examples are not intended to limit the present invention. Forexample, if the disclosure describes a first feature formed on or abovea second feature, that is, it may involve an embodiment in which thefirst feature contacts with the second feature directly, and may alsoinvolve an embodiment in which additional features are formed betweenthe first feature and the second feature, so that the first feature andthe second feature are not in direct contact with each other. Inaddition, different embodiments of the following description may use thesame reference symbols and/or labels. These repetitions are for thepurpose of simplicity and clarity and are not intended to limit specificrelationships between the different embodiments and/or structures.

FIG. 1A is a schematic diagram showing an image detection deviceaccording to an exemplary embodiment of the present invention. The imagedetection device 10 comprises a data storage device 100, a processor200, a displayer 300, a communication device 400, a sensor 500, anotification device 600, and an audio receiving device 700. The imagedetection device 10 can be an independent electronic device or built ina mobile electronic device (such as a mobile phone, a tablet computer, anotebook computer, a game device, an e-book or a PDA), a desktopcomputer, a server, or any electronic device equipped with a touchmodule (for example, a touch integrated circuit). The data storagedevice 100 can comprise storage units and can be implemented by a randomaccess memory (RAM), a read only memory (ROM), a flash memory (Flash), ahard disk, a floppy disk, a magnetic memory, an optical disc (CD), adigital video disc (DVD), etc.

Moreover, the processor 200 of the image detection device 10 is coupledto the data storage device 100 for accessing the data in the datastorage device 100. The processor 200 can comprise a digital signalprocessing (DSP), a microprocessor (MCU), a single central processingunit (CPU), or a plurality of parallel processing units related to aparallel processing environment for executing an operation system,modules, and applications. The displayer 300 is used to display the datain the data storage device 100. The displayer 300 can be, for example, aprojection displayer, a stereoscopic imaging displayer, an organiclight-emitting diode displayer, an electronic paper, a system integratedpanel, a light-emitting diode displayer, a liquid crystal screen, or atouch display panel, such as a resistive touch panel, a capacitive touchpanel, an optical touch panel, or an electromagnetic touch panel. Theaudio receiving device 700 is a device, such as a microphone, forreceiving the user's voice.

The communication device 400 supports a wireless communication protocolfor data transmission with another electronic device. For example, thewireless communication protocol may comprise GSM, GPRS, EDGE, UMTS,W-CDMA, CDMA2000, TD-CDMA, Bluetooth, NFC, WiFi, Wi-Fi Direct, WiMAX,LTE, LTE-A, or TD-LTE. The sensor 500 is configured to receive anoptical signal, convert the optical signal into an electrical signalsuch as a pixel, and transmit the electrical signal to the processor 200for calculation. For example, the sensor 500 may comprise an activepixel sensor (APS), a CMOS image sensor, a photosensitive couplingelement (CCD), an infrared sensing element, a phototransistor, variousoptical lenses, or the like. Therefore, the image of the user can bedetected by the sensor 500 even in a dim or dark environment. Thenotification device 600 is configured to play sound or emit light whenthe processor 200 determines that the user is in a dangerous position,for example, when the processor 200 determines that the user is sleepingin the prone position, to notify the other person of the dangerousposition of the user, thereby achieving the purpose of caring for theuser. For example, the notification device 600 may comprise an alarm, abuzzer, a warning light, a flasher, or an acousto-optic horn, or thelike. In another embodiment, the notification device 600 transmits orpushes a warning message to the electronic device held by the user'sfamily through the communication device 400 in the wirelesstransmission. For example, the warning message may be a text message ora voice message. Moreover, the electronic device held by the user'sfamily may also be pre-installed with a software application (app) forreceiving the warning message and receiving the images of the user.

FIG. 1B is a schematic diagram showing an image detection device 10according to another exemplary embodiment of the present invention. Inthis embodiment, the image detection device 10 is implemented by aseparate design, that is, the image detection device 10 is composed oftwo image detection devices 10A and 10B. The two image detection devices10A and 10B connect to and communicate with each other in a wire orwireless manner. The image detection device 10A comprises a processor200, an audio receiving device 700, a sensor 500, and a communicationdevice 400A. The image detection device 10B comprises a data storagedevice 100, a processor 200, a displayer 300, a communication device400B, and a notification device 600. The notification device 600 can bedisposed in the image detection device 10A. The present invention doesnot intend to limit the position of the notification device 600 in theimage detection device 10.

In detail, the image detection device 10A is installed in an environmentin which the user is located (for example, a bedroom), and the imagedetection device 10B serves as a host. For example, the image detectiondevice 10B can be a server, a mainframe, or a cloud host of themanufacturer of the image detection device 10. The images sensed by thesensor 500 of the image detection device 10A are transmitted to theimage detection device 10B through the communication devices 400A and400B for analysis.

In one embodiment, the sensor 500 is configured to capture a pluralityof images of a user. The processor 200 determines whether the user movesaccording to the images and obtains a plurality of feature parameters ofthe images. In detail, the processor 200 calculates pixels included ineach of the images, performs a subtraction operation on each set of twoadjacent images to obtain a pixel difference, and then determineswhether the user moves according to the pixel difference. If the pixeldifference is greater than a predetermined pixel value, the processor200 determines that the user has moved; if the pixel difference is lessthan or equal to the predetermined pixel value, the processor 200determines that the user has not moved.

Then, the processor 200 performs a body distribution analysis and a faceocclusion analysis on the images according to the feature parameters todetermine the position of the user. For example, the feature parameterscomprise the user's torso, face, head, eyes, nose, mouth, ears, hands,feet, and the distance between the center of the user's face and thecenter of the user's head. The face occlusion analysis is performed byusing the user's eyes, nose, and mouth as the feature parameters todetermine whether a face occlusion status has occurred on the user. Inaddition, the body distribution analysis is used to determine theposition of the user and whether the user is sleeping in the supineposition, on either side, in the prone position, or in another sleepingposition.

FIG. 1C is a schematic diagram showing the processor 200 and thenotification device 600 according to another exemplary embodiment of thepresent invention. The processor 200 comprises a data marking module 202and a feature analysis module 204. The data marking module 202 marks aplurality of feature parameters in the images captured by the sensor500. The feature analysis module 204 is configured to calculate andanalyze the detection result related to the feature parameters in eachimage. For example, the feature analysis module 204 determines whetherthe user's nose is occulted or calculates the distance between thecenter of the user's face and the center of the user's head.

In addition, the feature analysis module 204 comprises a bodydistribution analysis module 206 and a face occlusion analysis module208 for performing the body distribution analysis and the face occlusionanalysis, respectively. The feature parameters used for bodydistribution analysis may be different from the feature parameters usedfor the face occlusion analysis. In an embodiment, the processor 200first analyzes the feature parameters of the user by using the bodydistribution analysis module 206 and then uses the face occlusionanalysis module 208 to analyze the feature parameters of the user toaccurately determine the position and face occlusion status of the user.

If the confidence level of the above analysis is lower than apredetermined confidence value, it indicates that the reliability of theanalysis is insufficient, and the processor 200 will not adopt theresult of the body distribution analysis. At this time, the processor200 determines the position of the user through the face occlusionanalysis module 208. If the confidence value of the above analysis ishigher than or equal to the predetermined confidence value, it indicatesthat the reliability of the analysis is sufficient, and the processor200 will use the result of the body distribution analysis and performthe auxiliary determination through the face occlusion analysis.Therefore, the double analyses related to the body distribution and faceocclusion can improve the accuracy of the determination of the positionof the user.

In other words, the body distribution analysis module 206 performs thebody distribution analysis based on the detection results of the featureparameters and initially determines the position of the user (i.e., thefirst position information). Then, the face occlusion analysis module208 performs the face occlusion analysis on the images according to thedetection results of the feature parameters and the first positioninformation to obtain the second position information of the user. Itshould be noted that the second position information may be differentfrom the first position information, and the second position informationrepresents the final determination result of the position of the user.

FIG. 2 is a flow chart showing an image detection method according to anexemplary embodiment of the present invention. In step S200, theprocessor 200 activates the image detection device 10 and sets a regionof interest (ROI). In an embodiment, the image detection device 10 isused to determine the sleeping position of the user, and thus the regionof interest ROI is set as the bed where the user lies. The region ofinterest ROI may be preset by the image detection device 10 or may beset by the user according to the environment. When the setting iscomplete, in step S201, the sensor 500 starts shooting to capture aplurality of images related to the user. For example, the sensor 500takes 10˜30 images per second to record and detect the position of theuser in the ROI. The number of images captured by the sensor 500 persecond is for illustrative purposes only and is not intended to limitthe invention. In detail, the sensor 500 can adjust the number of imagescaptured per second according to the user environment and the needs ofthe user. For example, when the user needs to increase the accuracy ofthe determination, the number of images captured by the sensor 500 persecond can be increased.

In step S202, the processor 200 determines whether a first image isobtained. In detail, the processor 200 defines the first image as areference image which is a comparison reference for determining theposition of the user. In step S203, the processor 200 determines aplurality of feature parameters related to the user. Then, in step S204,the processor 200 determines whether a plurality of images of the userin the region of interest are obtained. In detail, when the sensor 500captures other images other than the reference image, the processor 200performs a subtraction operation on the other images and the referenceimage to obtain a difference and determines whether the user moves.

Then, in step S206, the processor 200 divides each of the above imagesinto a plurality of region bases. In step S208, the processor 200performs the body distribution analysis and face occlusion analysis onthe images to calculate the detection results of the feature parametersin each region base. Finally, in step S210, the processor 200 determinesthe position of the user according to the detection results.

FIG. 3 is a schematic diagram showing a plurality of region basesaccording to an exemplary embodiment of the present invention. Thefeature analysis module 204 of the processor 200 divides the image ofthe user into a plurality of region bases RB1˜RB4. As shown in FIG. 3,the region base RB1 corresponds to the user's body, the region base RB2corresponds to the user's head, the region base RB3 corresponds to theuser's face, and the region base RB4 corresponds to the user's hand.

Then, the feature analysis module 204 analyzes the detection results ofthe feature parameters in each of the region bases RB1˜RB4. The featureparameters include the user's torso, face, head, eyes, nose, mouth,ears, hands, feet, and the distance between the center of the user'sface and the center of the user's head. Therefore, the feature analysismodule 204 can detect and determine whether the feature parametersappear in the region bases RB1˜RB4.

In detail, the feature analysis module 204 comprises the bodydistribution analysis module 206 and the face occlusion analysis module208. For example, the body distribution analysis module 206 belongs tothe human body feature extraction model which is a region-basedconvolutional neural network (CNN) for identifying the features of eachregion base. In order to reduce the amount of data calculation andincrease the speed, the present invention adopts feature sharing betweena region generation network (RPN) and a feature extraction network tosimultaneously divides the region bases and extracts feature parameters.Furthermore, the above-mentioned human body feature extraction model canalso use a deep residual network (ResNet) as a feature extractionnetwork to reduce the memory usage and improve the efficiency of thefeature extraction.

FIGS. 4A and 4B are schematic diagrams showing marking of featureparameters according to an exemplary embodiment of the presentinvention. In an embodiment, the data marking module 202 of theprocessor 200 marks feature parameters such as the user's face or facialfeature from the image data collected by the sensor 500. Then, accordingto the feature parameters marked by the data marking module 202, thefeature analysis module 204 determines the position of the user andfurther determines whether a face occlusion status occurs.

First, a feature such as the user's face or a facial feature is manuallymarked in the image using a label. Through the learning, training andevaluation of the neural network, the data marking module 202 canautomatically and intelligently mark the feature parameters in an image.After the marking is complete, the corresponding script is automaticallybuilt in the training database and the evaluation database.

As shown in FIGS. 4A and 4B, the image of the user includes two regionalbases RB1 and RB2 representing the user's face and head respectively,wherein the center point of the face is represented as X1, and thecenter point of the head is represented as X2. The data marking module202 then marks the eyes, nose, mouth, and the like with labels LB. Thefeature analysis module 204 extracts various feature parametersaccording to the labels LB, such as the user's torso, face, head, eyes,nose, mouth, ears, hands, feet, the distance FD between the center ofthe user's face and the center of the user's head, and so on. If thedistance FD between the center of the user's face and the center of theuser's head is short, it means that the user is in a frontal-faceposition (i.e., the face is facing upwards); if the distance FD betweenthe center of the user's face and the center of the user's head isgreater, it means the user is in a lateral-face position.

FIG. 5 is a schematic diagram showing feature parameters being obtainedfrom an image of the user according to an exemplary embodiment of thepresent invention. The feature analysis module 204 provided in thepresent application comprises the body distribution analysis module 206and a face occlusion analysis module 208. The face occlusion analysismodule 208 can be an occlusion determination model which comprisesneural networks for two stages. The first-stage neural network comprisesthree lightweight mobile networks (MobileNet) that perform trainingrespectively for the user's eyes, nose and mouth to accurately determinethe positions of the user's eyes, nose and mouth as three featureparameters.

Moreover, the second-stage neural network is a fully connected network.The second-stage neural network receives the feature parametersextracted by the first-stage neural network and performs evaluation andtraining related to whether the occlusion status occurs.

In addition, the body distribution analysis module 206 and the faceocclusion analysis module 208 described in the present invention can bedeveloped based on the Tensorflow application framework or other deeplearning application frameworks and further use a graphics processingunit (GPU) to achieve effect of accelerated operations. As shown in FIG.5, by using the feature analysis module 204 described in the presentinvention, the feature parameter PF of the face, the feature parameterPE of the eyes, the feature parameter PN of the nose, and the featureparameter PM of the mouth can be extracted for each of the variousimages PIC. Then, the face occlusion analysis module 208 determineswhether the occlusion status occurs for each of the feature parameters.

In another embodiment, the processor 200 may, according to the featureparameters, determine whether the image clearly shows the user's face,and it may also determine a plurality of feature vectors in order todetermine the body position and the position type of the user. Then, theprocessor 200 selects a representative image related to a certainposition type according to the above determination results. Thecommunication device 400 transmits the representative image to theuser's family, relatives, or friends.

For example, the feature vectors include the user's voice, the anglesbetween the user's face and hands, the distances between the face andthe hands, the angles between the user's face and the feet, and thedistances between the face and the feet. The user's voice as received bythe audio receiving device 700 may indicate laughter or crying.Therefore, the processor 200 can determine the state and mood of theuser according to the user's voice and determine the body position andposition type through the combination of using the body distributionanalysis and the face occlusion analysis. FIGS. 6A and 6B are schematicdiagrams showing feature vectors according to an exemplary embodiment ofthe present invention. As shown in FIG. 6A, the image includes fourfeature vectors, which are the angle A1 between the user's face andright hand, the angle A2 between the user's face and left hand, theangle A3 between the user's face and right foot, and the angle A4between the user's face and left foot, wherein the angle A1 is about 297degrees, the angle A2 is about 123 degrees, the angle A3 is about 343degrees, and the angle A4 is about 4 degrees.

In addition to the angles, distances can also be used as featurevectors, and then different position types of the user can bedistinguished. As shown in FIG. 6B, the image includes five featurevectors, which are the distance D1 between the user's face and righthand, the distance D2 between the user's face and left hand, thedistance D3 between the user's face and right foot, the distance D4between the user's face and left foot, and the distance D5 between theuser's face and head, wherein, the distance D1 is about 358 pts (pixel),the distance D2 is about 99 pts, the distance D3 is about 250 pts, thedistance D4 is about 500 pts, and the distance D5 is about 45 pts.

In an embodiment, the processor 200 sets a predetermined number ofposition types. First, the processor 200 determines whether one imageclearly shows the user's face. If yes, the processor 200 determines theposition type to which the image belongs according to the above featurevectors and the results of the body distribution analysis and the faceocclusion analysis. Then, for each of the position types, the image inwhich the distance between the center of the face and the center of thehead is shortest is selected as the representative image for thedetermined position type. In addition, the processor 200 periodicallyselects a representative image of each of the position types. Forexample, one representative image may be selected per day, or onerepresentative image may be selected every morning and evening.

FIG. 7 shows a flow chart of determination of a risk from imagesaccording to an exemplary embodiment of the present invention. In StepS700, the sensor 500 captures a plurality of images of a user. In StepS702, the processor 200 calculates pixels included in each image andperforms a subtraction operation on two images to obtain a pixeldifference. In Step S704, the processor 200 determines whether the pixeldifference is greater than a predetermined pixel value. If the pixeldifference is greater than the predetermined pixel value, Step S706 isperformed. In Step S706, the processor 200 determines that the positionof the user is a high-risk position. If the pixel difference is lessthan or equal to the predetermined pixel value, Step S708 is performed.In Step S708, the processor 200 determines whether the user's nose iscovered.

If the user's nose user is not covered, Step S710 is performed. In StepS710, the processor 200 determines that the position of the user is alow-risk position. Then, in Step S712, the sensor 500 captures images ofthe user at a first frequency, or connects to the feature analysismodule 204 at the first frequency to determine the position of the user.If the nose of the user is covered, Step S706 is performed. In StepS706, the processor 200 determines that the position of the user is ahigh-risk position. Next, in Step S714, the sensor 500 captures imagesof the user at a second frequency or connects to the feature analysismodule 204 at the second frequency to determine the position of theuser. The second frequency is higher than the first frequency.Therefore, when the high-risk position is determined, the imagedetection device 10 of the present invention captures images anddetermines the position of the user at a higher frequency, therebyaccurately detecting the position of the user early to prevent danger.

FIG. 8 shows a flow chart of a body distribution analysis and a faceocclusion analysis according to an exemplary embodiment of the presentinvention. Performing two different analyses, a body distributionanalysis and a face occlusion analysis, can effectively improve theaccuracy of the determination of the position of the user. In Step S800,the sensor 500 captures a plurality of images of a user. In Step S802,the data marking module 202 marks a plurality of feature parameters inthe images. In Step S804, the processor 200 calculates and evaluates thedetection results of the feature parameters in each of the above images.

Next, in step S806, the processor 200 performs a body distributionanalysis on the images according to the detection result of at least onefirst feature parameter of the feature parameters to determine firstposition information of the user. It should be noted that in order toimprove the accuracy of the determination, the processor 200 may performa face occlusion analysis to assist in determining the position of theuser. In Step S808, the processor 200 performs a face occlusion analysison the images according to the detection result of at least one secondfeature parameter of the feature parameters and the first positioninformation to determine second position information of the user. Sincethe second position information is determined to correct the firstposition information, it can be used to determine the actual position ofthe user.

FIGS. 9A-9E show a flow chart of determination of a position of a useraccording to a body distribution analysis and a face occlusion analysisaccording to an exemplary embodiment of the present invention. In StepS900, the sensor 500 captures a plurality of images of a user. In StepS902, the processor 200 determines a plurality of first featureparameters and a second feature parameter of the images to determine theposition of the user. Depending on the selected first feature parameter,Step S904, Step S918, Step S942, or Step S954 may be performed next,which will be described separately in the following paragraphs.

In Step S904, the processor 200 sets a first feature parameter to thedistance between the center of the user's face and the center of theuser's head. In Step S906, the processor 200 determines whether thedistance is less than a predetermined distance value. For example, theabove predetermined distance value is 60 pts. If the distance is lessthan the predetermined distance value, Step S908 is performed. In StepS908, the processor 200 determines that the user is sleeping in thesupine position. If the distance is greater than or equal to thepredetermined distance value, Step S910 is executed. In Step S910, theprocessor 200 determines that the user is sleeping on his side.

Then, in Step S912, the processor 200 sets a second feature parameter tothe user's eyes. In Step S914, the processor 200 determines whether bothof the eyes are detected at the same time. If both of the eyes aredetected, Step S916 is performed. In Step S916, the processor 200determines that the user is sleeping in the supine position. If both ofthe eyes are not detected at the same time, Step S910 is performed. InStep S910, the processor 200 determines that the user is sleeping on hisside.

In another embodiment, as shown in Step S918, the processor 200 setsfirst feature parameters to the user's face, head, and ears. In StepS920, the processor 200 determines whether the user's head and ears aredetected but the user's face is not detected. If yes, the methodproceeds to Step S922; if no, the method proceeds to Step S924.

In S922, the processor 200 determines that the user is sleepingpartially in the prone position. Next, in Step S930, the processor 200sets the second feature parameter to the user's eyes. In Step S932, theprocessor 200 determines whether both of the eyes are detected at thesame time. If both of the eyes are detected, Step S934 is performed. InStep S934, the processor 200 determines that the user is sleeping in thesupine position. If both of the eyes are not detected at the same time,Step S922 is performed. In Step S922, the processor 200 determines thatthe user is sleeping partially in the prone position.

In addition, in Step S924, the processor 200 determines whether the headis detected but the face and the ears are not detected. If no, themethod proceeds to Step S928 in which the processor 200 determines thatthe user is sleeping in another position; if yes, the method proceeds toStep S926 in which the processor 200 determines that the user issleeping completely in the prone position. Then, Step S936 is performed.In Step S936, the processor 200 sets the second feature parameter to theuser's eyes. In Step S938, the processor 200 determines whether one ofthe eyes is detected. If yes, the method proceeds to Step S940 in whichthe processor 200 determines that the user is sleeping on his side; ifno, the method proceeds to Step S926 in which the processor 200determines that the user is sleeping completely in the prone position.

In another embodiment, as shown in Step S942, the processor 200 setsfirst feature parameters as the user's nose, eyes, and mouth. In StepS944, the processor 200 determines whether the nose is not detected. Ifyes, the method proceeds to Step S946 in which the processor 200determines that the user is covered dangerously. At this time, theprocessor 200 transmits a warning message through the notificationdevice 600 to notify the user's family, relatives, or caregivers of thisface occlusion status; if no, the method proceeds to Sep S948.

In Step S948, the processor 200 determines whether the nose, eyes, andmouth are detected. If the nose, eyes, and mouth are detected, themethod proceeds to Step S950 in which the processor 200 determines thatthe user is not covered. Moreover, if the nose, eyes and mouth are notdetected, the method proceeds to Step S952 in which the processor 200determines that the user is sleeping in another position.

In another embodiment, as shown in Step S954, the processor 200 setsfirst feature parameters as the user's trunk, head, and face. In StepS956, the processor 200 determines whether the trunk is detected but thehead and face are not detected. If yes, the method proceeds to Step S958in which the processor 200 determines that the user's head is covered;if no, the method proceeds to Step S960.

In Step S960, the processor 200 determines whether the trunk, the head,and the face are not detected. If the trunk, head and face are notdetected, the method proceeds to Step S962 in which the processor 200determines that either the body of the user is fully covered, or theuser is not in bed. If the trunk, the head and the face are detected,the method proceeds to Step S964 in which the processor 200 determinesthat the user is sleeping in another position.

FIG. 10 shows a flow chart of determination of a representative imagefor a position type by using an image detection method according to anexemplary embodiment of the present invention. In Step S1002, the imagedetection device 10 obtains a plurality of images and a plurality offeature parameters of a user. In Step S1004, the processor 200 sets apredetermined number of position types. In Step S1006, the processor 200performs a face occlusion analysis on the image according to the featureparameters.

Then, in Step S1008, the processor 200 determines a plurality of featurevectors and performs a body distribution analysis on the imagesaccording to the feature vectors to determine the body position andposition type of the user. In Step S1010, the processor 200 selects animage regarding the position based on the results of the face occlusionanalysis and the body distribution analysis. In Step S1012, theprocessor 200 determines whether the above image clearly shows theuser's face.

If the image does not clearly shows the user's face, the method returnsto Step S1010, and the processor 200 selects another image regarding thetype of the position. If the image clearly shows the user's face, methodproceeds to Step S1014 in which the processor 200 calculates thedistance between the center of the face and the center of the head inthe image. Then, in step S1016, the processor 200 selects the image inwhich the distance between the center of the face and the center of thehead is shortest as a representative image for the position type. InStep S1018, the processor 200 transmits the representative image.

The ordinal numbers in the specification and the scope of the patentapplication, such as “first”, “second”, “third”, etc., have nosequential relationship with each other, and are only used todistinguish between two different components with the same name. Theterm “coupled” in this specification refers to a variety of direct orindirect electrical connections.

While the invention has been described by way of example and in terms ofthe preferred embodiments, it should be understood that the invention isnot limited to the disclosed embodiments. On the contrary, it isintended to cover various modifications and similar arrangements (aswould be apparent to those skilled in the art). Therefore, the scope ofthe appended claims should be accorded the broadest interpretation so asto encompass all such modifications and similar arrangements.

What is claimed is:
 1. An image detection method utilizing dualanalysis, comprising: obtaining a plurality of images of a user; markinga plurality of feature parameters in the plurality of images; evaluatingdetection results of the plurality of feature parameters in each of theplurality of images; performing a body distribution analysis on theplurality of images according to the detection result of at least onefirst feature parameter among the plurality of feature parameters todetermine first position information of the user; and performing a faceocclusion analysis on the plurality of images according to the detectionresult of at least one second feature parameter among the plurality offeature parameters and the first position information to determinesecond position information of the user, wherein the at least one secondfeature parameter is different from the at least one first featureparameter, and the second position information represents a position ofthe user.
 2. The image detection method as claimed in claim 1, whereinthe plurality of feature parameters comprise the user's trunk, face,head, eyes, nose, mouth, ears, hands, feet, and a distance betweencenter of the face and center of the head.
 3. The image detection methodas claimed in claim 2, further comprising: setting the at least onefirst feature parameter to the distance between the center of the faceand the center of the head; in response to the distance between thecenter of the face and the center of the first position informationindicates that the user is sleeping in a supine position; and inresponse to the distance between the center of the face and the centerof the head being equal to or greater less the predetermined distancevalue, determining that the first position information indicates thatthe user is sleeping on his side.
 4. The image detection method asclaimed in claim 3, further comprising: in response to determining thatthe first position information indicates that the user is sleeping onhis side, setting the at least one feature parameter to the eyes; and inresponse to detecting the eyes, determining the second positioninformation indicates that the user is sleeping in the supine position.5. The image detection method as claimed in claim 2, further comprising:setting the at least one first feature parameter to the face, the head,and the ears; in response to detecting the head and the ears but notdetecting the face, determining that the first position informationindicates that the user is sleeping partially in a prone position; andin response to detecting the head but not detecting the face and ears,determining that the first position information indicates that the useris sleeping completely in the prone position.
 6. The image detectionmethod as claimed in claim 5, further comprising: in response todetermining that the first position information indicates that the useris sleeping partially in the prone position, setting the at least onesecond feature parameter to the eyes; and in response to detecting theeyes, determining that the second position information indicates thatthe user is sleeping in the supine position.
 7. The image detectionmethod as claimed in claim 5, further comprising: in response todetermining that the first position information indicates that the useris sleeping completely in the prone position, setting the at least onesecond feature parameter to the eyes; in response to detecting the eyes,determining that the second position information indicates that the useris sleeping in the supine position; and in response to detecting one ofthe eyes, determining that the second position information indicatesthat the user is sleeping on his side.
 8. The image detection method asclaimed in claim 2, further comprising: setting the at least one firstfeature parameter to the nose, the eyes, and the mouth; and in responseto not detecting the nose, not detecting the eyes and the nose, or notdetecting the mouth and the nose, determining that the first positioninformation indicates that the user is covered dangerously.
 9. The imagedetection method as claimed in claim 2, further comprising: setting theat least one first feature parameter to the nose, the eyes, and themouth; and in response to detecting the nose, the eyes, and the mouth,determining that the first position information indicates that the useris not covered.
 10. The image detection method as claimed in claim 2,further comprising: setting the at least one first feature parameter tothe trunk, the head, and the face; and in response to detecting thetrunk but not detecting the head and the face, determining that thefirst position information indicates that the head is covered.
 11. Theimage detection method as claimed in claim 2, further comprising:setting the at least one first feature parameter to the trunk, the head,and the face; and in response to not detecting the trunk, the head, andthe face, determining that the first position information indicates thateither the body of the user is fully covered, or the user is not in bed.12. An image detection device utilizing dual analysis, comprising: asensor capturing a plurality of images of a user; a data marking modulemarking a plurality of feature parameters in the plurality of images;and a feature analysis module calculating detection results of theplurality of feature parameters in each of the plurality of images,wherein the feature analysis module comprises: a body distributionanalysis module performing a body distribution analysis on the pluralityof images according to the detection result of at least one firstfeature parameter among the plurality of feature parameters to determinefirst position information of the user; and a face occlusion analysismodule performing a face occlusion analysis on the plurality of imagesaccording to the detection result of at least one second featureparameter among the plurality of feature parameters and the firstposition information to determine second position information of theuser, wherein the at least one second feature parameter is differentfrom the at least one first feature parameter, and the second positioninformation represents a position of the user.